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Record W4378072116 · doi:10.2166/washdev.2023.029

Promoting safe and inclusive water and sanitation services for students with physical disabilities in primary schools: a concept mapping study in Ghana

2023· article· en· W4378072116 on OpenAlexaff
Urbanus Wedaaba Azupogo, Ebenezer Dassah, Elijah Bisung

Bibliographic record

VenueJournal of Water Sanitation and Hygiene for Development · 2023
Typearticle
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsQueen's University
Fundersnot available
KeywordsSanitationUniversal designPsychological interventionGovernment (linguistics)HygieneMedical educationPhysical accessPsychologyInclusion (mineral)BusinessPublic relationsNursingPolitical scienceMedicineEngineeringSocial psychologyComputer securityComputer science

Abstract

fetched live from OpenAlex

Abstract Improving water, sanitation, and hygiene (WASH) services in schools is crucial to providing inclusive environments for all children to thrive. Particularly for children with disabilities, the school environment can serve as a barrier to their access to and use of WASH facilities. This paper examines strategies and programs needed to promote safe and inclusive WASH services for students with physical disabilities in primary schools in Ghana. We recruited 22 stakeholders from the Upper West Region of Ghana to complete an online concept mapping exercise. Participants identified eight themes for promoting safe and inclusive access to WASH services for students with physical disabilities. These themes included ‘building special schools,’ ‘guidance services,’ ‘ensuring non-discrimination and fair treatment,’ ‘additional programs for PWDs,’ ‘local government interventions,’ ‘public sensitization,’ ‘teacher training,’ and ‘supervision.’ These findings can assist stakeholders in identifying strategies and prioritising programs in the short run to improve WASH among students with physical disabilities.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.618
Threshold uncertainty score0.331

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.020
GPT teacher head0.309
Teacher spread0.289 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2023
Admission routes1
Has abstractyes

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